code: def forward(self, g, feats):
      feat_lst = []
      for layer in self.layers:
          feats = self.dropout(layer(g, feats))
          feat_lst.append(feats)
  
      if self.mode == "lstm":
          self.jump.lstm.flatten_parameters()
  
      g.ndata["h"] = self.jump(feat_lst)
      g.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
  
      return self.output(g.ndata["h"])
  
file_path: dgl/examples/pytorch/jknet/model.py
imports: import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import GraphConv, JumpingKnowledge
label_desc: Use image node_id 3 for calling the JKNet obj's underlying member method code with example usage: obj.forward(g, feats) and returns: self